Literature DB >> 15697817

Consistency of nonlinear system response to complex drive signals.

Atsushi Uchida1, Ryan McAllister, Rajarshi Roy.   

Abstract

The consistency of a nonlinear system's response to a repeated complex waveform drive signal is an important consideration in classical and quantum systems as diverse as lasers, neuronal networks, and manufacturing plants. We show from a consideration of different characteristic waveforms that there is typically an optimal drive amplitude for the most consistent response; internal noise sources dominate for small amplitude driving while deterministic system nonlinearity reduces consistency for large amplitudes. We test this general concept and its measurement experimentally and numerically on the specific example of a laser system.

Mesh:

Year:  2004        PMID: 15697817     DOI: 10.1103/PhysRevLett.93.244102

Source DB:  PubMed          Journal:  Phys Rev Lett        ISSN: 0031-9007            Impact factor:   9.161


  7 in total

1.  Functional brain networks: great expectations, hard times and the big leap forward.

Authors:  David Papo; Massimiliano Zanin; José Angel Pineda-Pardo; Stefano Boccaletti; Javier M Buldú
Journal:  Philos Trans R Soc Lond B Biol Sci       Date:  2014-10-05       Impact factor: 6.237

2.  Chaos and reliability in balanced spiking networks with temporal drive.

Authors:  Guillaume Lajoie; Kevin K Lin; Eric Shea-Brown
Journal:  Phys Rev E Stat Nonlin Soft Matter Phys       Date:  2013-05-06

Review 3.  Minimal approach to neuro-inspired information processing.

Authors:  Miguel C Soriano; Daniel Brunner; Miguel Escalona-Morán; Claudio R Mirasso; Ingo Fischer
Journal:  Front Comput Neurosci       Date:  2015-06-02       Impact factor: 2.380

4.  Reservoir Computing Beyond Memory-Nonlinearity Trade-off.

Authors:  Masanobu Inubushi; Kazuyuki Yoshimura
Journal:  Sci Rep       Date:  2017-08-31       Impact factor: 4.379

5.  Encryption key distribution via chaos synchronization.

Authors:  Lars Keuninckx; Miguel C Soriano; Ingo Fischer; Claudio R Mirasso; Romain M Nguimdo; Guy Van der Sande
Journal:  Sci Rep       Date:  2017-02-24       Impact factor: 4.379

6.  Physical Implementation of Reservoir Computing through Electrochemical Reaction.

Authors:  Shaohua Kan; Kohei Nakajima; Tetsuya Asai; Megumi Akai-Kasaya
Journal:  Adv Sci (Weinh)       Date:  2021-12-29       Impact factor: 16.806

7.  Photonic reinforcement learning based on optoelectronic reservoir computing.

Authors:  Kazutaka Kanno; Atsushi Uchida
Journal:  Sci Rep       Date:  2022-03-08       Impact factor: 4.996

  7 in total

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